AIMC Topic: Image Processing, Computer-Assisted

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Transfer learning for data-efficient abdominal muscle segmentation with convolutional neural networks.

Medical physics
BACKGROUND: Skeletal muscle segmentation is an important procedure for assessing sarcopenia, an emerging imaging biomarker of patient frailty. Data annotation remains the bottleneck for training deep learning auto-segmentation models.

Grayscale medical image segmentation method based on 2D&3D object detection with deep learning.

BMC medical imaging
BACKGROUND: Grayscale medical image segmentation is the key step in clinical computer-aided diagnosis. Model-driven and data-driven image segmentation methods are widely used for their less computational complexity and more accurate feature extractio...

Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care.

Contrast media & molecular imaging
Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP syste...

A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network.

Journal of healthcare engineering
BACKGROUND: The liver is one of the most significant and most essential organs in the human body. It is divided into two granular lobes, one on the right and one on the left, connected by a bile duct. The liver is essential in the removal of waste pr...

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Journal of healthcare engineering
Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and r...

A novel convolutional neural network for kidney ultrasound images segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Ultrasound imaging has been widely used in the screening of kidney diseases. The localization and segmentation of the kidneys in ultrasound images are helpful for the clinical diagnosis of diseases. However, it is a challeng...

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification.

BioMed research international
COVID-19 is a fatal disease caused by the SARS-CoV-2 virus that has caused around 5.3 Million deaths globally as of December 2021. The detection of this disease is a time taking process that have worsen the situation around the globe, and the disease...

Curv-Net: Curvilinear structure segmentation network based on selective kernel and multi-Bi-ConvLSTM.

Medical physics
PURPOSE: Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal w...

COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.

Scientific reports
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quanti...

Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory.

PloS one
The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute myo...